Context-sensitive encoding and decoding of a video data stream

ABSTRACT

Disclosed are methods and devices for compressing and decompressing video data streams, according to which the statistical relationship between image symbols and the context assigned thereto is used for compression. Particularly disclosed is a context-sensitive encoding unit in which the image symbols filed in an image storage are assigned to different encoding branches via a context switch, where they are encoded and compressed by a Golomb encoder and a run length encoder.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is based on and hereby claims priority to PCTApplication No. PCT/DE03/00306 filed on Feb. 4, 2003 and GermanApplication No. 102 04 617.4 filed on Feb. 5, 2002, the contents ofwhich are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

The invention relates to method for compression and decompression of avideo data stream. The invention further relates to devices forexecuting the method.

Compression methods of this type are especially necessary fortransporting video data over packet-oriented data networks since thebandwidth of packet-oriented data networks is tightly restricted.Standardized methods such as for example MPEG-1, MPEG-2 and H.26× havethus been developed with which video data can be compressed. Thestandardized methods operate with motion-compensating hybrid encoding, acombination of lossless redundancy reduction and lossy irrelevancereduction.

The greatest contribution to compression is made by what is known asmotion-compensating prediction. Motion-compensating prediction uses thesimilarity of consecutive images by predicting the current image to beencoded from images already transmitted. Since mostly only parts ofconsecutive images move, an encoder breaks down the current image to beencoded into rectangular macro blocks which are mostly 8×8 or 16×16pixels in size. For each of these macro blocks the encoder searches formatching macro blocks from the images already transmitted and calculateshow they have shifted in relation to the macro blocks of the currentimage to be encoded. The shifts in the macro blocks are described bymotion vectors which are encoded by the encoder on the basis of codetables.

Since the current image to be encoded cannot be constructed in everycase by the shifts in macro blocks of images already transmitted, forexample when new objects come into the image, the prediction error mustalso be transmitted from the encoder to the decoder. This predictionerror is the result of the difference between the actual current imageto be encoded and the prediction image constructed from shifts in macroblocks from previous images.

Since the prediction errors of adjacent pixels correlate in areas thatcannot be predicted or cannot be well predicted, a transformation of theprediction errors is undertaken for further redundancy reduction.Depending on the compression method, various transformation methods areemployed here. Typical normal methods are Discrete WaveletTransformation (DWT) or Discrete Cosine Transformation (DCT). DiscreteCosine Transformation transforms each macro block from 8×8 pixels into amatrix of 8×8 spectral coefficients. In this case the first coefficientrepresents the average brightness of the block, this also being referredto as the direct component or “DC coefficient”. The remainingcoefficients reflect with increasing index number the higher frequencycomponent of the brightness distribution and are thus referred to as“alternating components” or “AC coefficients”.

To reduce the required data rate further, the spectral coefficients arequantized before further encoding. When the prediction error signalchanges only slowly from pixel to pixel, most of the high-frequencycoefficients are equal to zero after quantizing and thus do not have tobe transmitted.

Since after transformation most spectral coefficients are zero, thespectral coefficients are grouped together during subsequent executionof the method by run length encoding and subsequently encoded with theaid of a code table with variable-length code words.

SUMMARY OF THE INVENTION

Starting from this related art, one possible object of the invention isto specify methods for compression and decompression of video datawhich, by comparison with known methods, feature a higher level ofcompression.

The inventors propose a method for compression of video data, in whichthe video data of an image is represented by image symbols, with thefollowing steps:

-   -   Reading image symbols out of an image memory;    -   Sorting the image symbols with the aid of a context switch onto        various encoding branches into image group symbols, which are        assigned to different contexts in each case, where the context        switch is moved into a prespecified position at a prespecified        time and is then activated depending on the relevant context of        the image symbol to be transmitted;    -   Entropy encoding of the image symbol groups and merging of the        data output in the encoding branches into a compressed video        data stream.

The inventors also propose a method for decompression of a compressedvideo data stream, in which video data of image symbols representing animage is extracted from the video data stream, by the following steps:

-   -   Dividing up the video data stream into bit stream segments which        are each assigned to a context;    -   Entropy decoding of the bit stream segments into image symbol        groups; and    -   Transmission of the image symbols in the image symbol groups        distributed over various decoding branches via a context switch        into an image memory, where the context switch is in a        prespecified position at a prespecified time and is then        activated in accordance with the context of the image symbols.

The method for compression and decompression is based on the knowledgethat the probability of an image symbol occurring can be stronglydependent on the relevant context. The method exploits this situation byhaving the image symbols sorted depending on the relevant context intothe encoding branches. The image symbols distributed on the codingbranches can then be effectively encoded with a code adapted to thefrequency distribution of the image symbols in the relevant context witha variable word length. Such a code is also referred to below as anentropy code. It is especially advantageous that a code matched to theactual frequency distribution of the image symbols in the relevantcontext can be used.

In a preferred embodiment binary symbols are distributed on the encodingbranches and subsequently subjected to run length encoding, in which thenumber of consecutive identical symbols is counted and encoded by anumber assigned to the symbol.

This embodiment of the method exploits the situation whereby in aspecific context a large number of identical symbols occur which can beeffectively encoded by run length encoding. The fact that the imagesymbols are sorted into the encoding branches depending on the relevantcontext, so that groups of image symbols are present in the encodingbranches, each of which features a large number of identical imagesymbols creates the condition for effective run length encoding.

With a further preferred embodiment an analytically calculable entropycode is used in the relevant encoding branch or decoding branch for thecode with variable word length which is adapted during the compressionor decompression process to the frequency distribution of the imagesymbols in the relevant context.

Use of an analytically generatable code enables permanently stored codetables containing a code adapted to any possible context to be dispensedwith. Instead the entropy codes used can easily be adapted to the actualfrequency distributions which occur. Precise adaptation to the frequencydistributions actually occurring allows efficient encoding of the imagesymbols which further reduces the bandwidth necessary for transmission.

In a further preferred embodiment the analytically calculable Golumbcode is used for encoding the image symbols.

The Golumb code is especially suited for ongoing adaptation to therelevant frequency distribution of the image symbols, since this code inis able to be continuously calculated depending on a single parameterand is therefore easy to parameterize. This code also offers theadvantage, by contrast with arithmetic encoding, of beingerror-tolerant.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects and advantages of the present invention willbecome more apparent and more readily appreciated from the followingdescription of the preferred embodiments, taken in conjunction with theaccompanying drawings of which:

FIG. 1 is a block diagram which shows the structure of an encoder andthe execution sequence of the method used for compression;

FIG. 2 is a diagram which shows the image symbols of a macro block of avideo image in which the image symbols to be transmitted and theassociated context are entered;

FIG. 3 is a block diagram of a context-sensitive encoding unit which atthe same time illustrates the execution of context-sensitive encoding;

FIG. 4 is a block diagram with the layout of a decoder, which at thesame time illustrates the execution sequence of the decoding method;

FIG. 5 is a block diagram of a context-sensitive decoder unit in whichthe execution sequence of the context-sensitive decoder method is alsoshown; and

FIG. 6 is a block diagram of a modified encoder.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

Reference will now be made in detail to the preferred embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings, wherein like reference numerals refer to like elementsthroughout.

FIG. 1 shows an encoder 1 which operates in accordance with theprinciple of the motion-compensating hybrid encoding. The encoder 1 hasan input 2 via which the video data stream is fed to the encoder 1. Inparticular video data of a sequence of images is fed via the input 2 tothe encoder. A motion estimation unit 3 segments the current image to beencoded of the video data stream into rectangular macro blocks which aremostly 8×8 or 16×16 pixels in size. For each of these macro blocks themotion estimation unit 3 looks for matching macro blocks from the imagesalready transmitted and calculates their motion vectors. The motionvectors can then be encoded with the aid of known code tables but alsowith the aid of a context-sensitive encoding unit 4 described in greaterdetail below and embedded via a multiplexer 5 into a bit stream outputat an output 6. The motion vectors of the macro blocks calculated by themotion estimation unit 3 are also notified to a motion compensator 7which, starting from the images already transmitted stored in an imagememory 8, calculates the prediction image produced by the shifts of themacro blocks of the images already transmitted. This prediction image issubtracted in a subtractor 9 from the original image in order to createa prediction error which is fed to a discrete cosine transformer 10 withdownstream quantizer 11. The error is also referred to as the predictionerror or texture. The transformed and quantized prediction error isforwarded to a further context-sensitive encoding unit 4 which convertsthe transformed and quantized prediction error data into bit streamsegments which are read out by the multiplexer 5 and embedded into thebit stream output at the output 6.

Processing in the discrete cosine transformer 10 represents the macroblocks with for example 8×8 pixels as a matrix of 64 spectralcoefficients. In this case the first coefficient contains the averagebrightness and is therefore also known as the direct component or DCcoefficient. The remaining spectral coefficients reflect higherfrequency components of the brightness distribution with increasingindex number, which is why they are referred to as alternatingcomponents or AC coefficients. The data rate is further reduced by thesubsequent quantizer 11. With planar image elements the prediction erroronly changes slowly from pixel to pixel, so that after processing inquantizer 11 most of the high-frequency spectral coefficients are equalto zero and thus do not even have to be transmitted.

The quantizer 11 additionally takes account of psychovisual effects.Since the human brain perceives low-frequency image components, namelyflat extended areas of image components, far more clearly thanhigh-frequency image components, namely details. Thus the high-frequencyspectral coefficients will be quantized more roughly than thelow-frequency spectral coefficients.

To adjust the images already transferred stored in the image memory 8,the spectral coefficients are fed to an inverse quantizer 12 and aninverse discrete cosine transformer 13 and the data reconstructed fromthe prediction error in this way added in an adder 14 to the predictionimage created by the motion compensator 7. The image thus createdcorresponds to the image produced on decoding. This image is stored inthe image memory 8 and is used by the motion estimation unit 3 as abasis for calculating the motion vectors of the following images.

The layout and the function of the context-sensitive encoding unit 4will be explained below with reference to FIGS. 2 and 3.

In FIG. 2 variables x₁ to x₆₄ are used to represent the spectralcoefficients output by quantizer 11. Spectral coefficient x₁ representsthe DC component or DC coefficient. The spectral coefficients x₈ and X₅₇are the spectral coefficients assigned in each case to the highest imagefrequencies in the x and y direction. Spectral coefficient x₆₄corresponds to the highest image frequency along the image diagonals. Ifthe coding unit 4 is used for encoding the motion vectors the variablesx₁ to x₆₄ can also represent different shift vectors.

The spectral coefficients generated by the discrete cosine transformerare ideally fully decorrelated, i.e. adjacent spectral coefficients arestatistically independent of one another. Even with an idealdecorrelation of the spectral coefficients this does not necessarilyapply to the individual bits in the bit levels of the spectralcoefficients. Instead a high degree of statistical dependencies existhere. This is based on the fact that the spectral coefficients generallyfeature small values, so in the area of the lower less significant bitlevels logical 1 is frequently present.

In FIG. 2 for example the spectral coefficient x₁₉ is highlighted forwhich the binary value at a specific bit level statistically depends onthe binary values of the spectral coefficients of a context group 15 atthe relevant bit level. In FIG. 2 the context group 15 is formed fromthe binary values of the spectral coefficients x₁ to X₃, x₉ to x₁₁, aswell as X₁₇ and x₁₈ at a specific bit level. The frequency distributionof the binary values for the spectral coefficient x₁₉ at the relevantbit level statistically depends on the binary values of the adjacentspectral coefficients at this bit level.

The values of the variables x_(i) are referred to as image symbolsbelow. The totality of the image symbols forms an alphabet. A specificcombination of values of the variables in a context group C is referredto for short as a context below.

The context-sensitive encoder unit 4 shown in detail below in FIG. 3exploits the statistical dependencies between the image symbols and therelevant assigned context for effective encoding.

In the context-sensitive encoder unit 4 the variables x_(i) are brokendown by a bit level scanner 16 into bit levels. The bit levels aretransferred consecutively, starting with the highest-order bit level,into a buffer memory 17. In this case this means that there are only twodifferent image symbols, namely logical 0 and logical 1.

The image symbols are read out serially in a prespecified sequence fromthe buffer memory 17. The image symbols read out from the buffer memory17 are sorted with the aid of a context-sensitive switch 18 ontoencoding branches 19. The context-sensitive switch 18 is controlled inthis case by a context discriminator 20 which determines for each imagesymbol the associated context and ensures that the image symbols areassigned to the encoding branches 19 accordingly. In addition to thecontext discriminator 20 there is also a cluster unit 21 present whichevaluates the frequency distribution of the image symbols in therelevant contexts and, if the frequency distribution of the imagesymbols in different contexts matches, causes the context discriminator20 to locally group the matching contexts as regards the frequencydistribution of the image symbols and assign then to a common encodingbranch 19.

The image symbols assigned to one of the encoding branches 19 areinitially stored in an image symbol buffer 22. Subsequently the imagesymbols are converted into run length encoding symbols with the aid ofrun length encoding 23 which converts the image symbols into run lengthencoding symbols. If the frequency distribution of the image symbolsdeviates from equal distribution in accordance with the relevantcontext, a few image symbols will occur especially frequently in theencoding branches 19. The same image symbols can however be groupedtogether effectively with the aid of run length encoding 23 into runlength symbols. With run length encoding consecutive equivalent imagesymbols are counted and encoded by the occurrence number. The binarysequence “000100” is encoded to “302” for example, where the “0” standsfor the binary “1”.

In further execution the run length symbols are converted by a Golumbencoder 24 into code symbols with different codeword lengths. The Golumbcode as such is known to the expert and not discussed in thisapplication.

Golumb encoding is especially suitable for encoding run length symbolssince run length encoding of the binary image symbols produces anpractically geometrical distribution of the run length symbols. Golumbencoding is particularly suitable for this type of distribution.

Since the frequency distribution of the image symbols is continuouslydetermined by the cluster unit 21, the cluster unit 21 can control theGolumb encoder 24 in such as way that the Golumb code used by the Golumbencoders 24 for encoding the run length symbols is matched to thefrequency distribution of the image symbols occurring a specificcontext.

In this context to the Golumb code is of particular advantage. This isbecause the Golumb code is especially suited for an adjustment to thechanging frequency distribution of the image symbols in a specificcontext since parameters can be set for the Golumb code and with the aidof an individual parameter it can be adapted to the changing frequencydistribution of symbols in an alphabet.

The code symbols generated by the Golumb encoder 24 are stored as bitstream segments in a bit stream buffer 25. A multiplexer 26 merges thebit stream segments of the individual encoding branches 19 into a singlebit stream segment, where the individual bit stream segments of theencoding branches 19 are each provided with a “header” into which thelength of the relevant bit stream segment is entered.

The function of the multiplexer 26 can also be taken over by multiplexer5 which provides a compressed video data stream at output 6 in which theinformation about the shift vectors and the prediction error data isembedded.

In summary this enables the following procedural steps listed in thecontext-sensitive encoding unit 4 to be adhered to:

-   -   Serial reading out of the image symbols from an image memory 15;    -   Sorting the image symbols with the aid of a context switch on        various encoding branches into image group symbols which are        assigned to different contexts in each case, where the context        switch is moved into a prespecified position at a prespecified        time and is then activated depending on the relevant context of        the image symbol to be transmitted;    -   Run length encoding of the image symbols distributed on encoding        branches 19 into run length symbols;    -   Conversion of the run length symbols with the aid of an entropy        code adapted to the entropy of the relevant context into code        symbols which are merged into a bit stream segment;    -   Combination of the bit stream segments by multiplexer 26 into a        compressed video data stream.

In a further exemplary embodiment not shown the context is not selectedat bit level but using the full spectral coefficients. Selecting acontext at full spectral coefficient level makes sense if, as a resultof the image statistics, processing by the discrete cosine transformer10 does not represent the ideal decorrelating transformation so that thespectral coefficients are not completely decorrelated.

In practice discrete cosine transformation is not the idealdecorrelating transformation, so that statistical dependencies existbetween the spectral coefficients which can be exploited by sortingaccording to contexts and subsequent Golumb encoding. In this casehowever the run length encoding mentioned is not effective since thefull spectral coefficients can assume a large number of values, so thatan extremely extensive alphabet is produced. Since in this case theoccurrence frequencies of the image symbols observed in accordance withthe relevant contexts follow almost geometrical distributions, sortingin accordance with contexts and Golumb encoding adapted to the relevantcontext is entirely sensible.

In addition it is also possible for decorrelated spectral coefficientsof a macro block to sort the spectral coefficients so that for selectionof the context group only the spectral coefficients of the samefrequency, for example all spectral coefficients x₁, from the adjacentmacro blocks are considered. This allows statistical dependenciesbetween the spectral coefficients to be established.

The associated decoding method is considered below. It is taken as readthat a decoding method must feature the corresponding steps. Such adecoding method will now be explained with reference to FIGS. 4 and 5.

FIG. 4 shows a decoder 27 for the video data stream generated by encoder1. The decoder 27 receives the video data stream at an input 28 to whichdemultiplexer 29 is connected downstream. The demultiplexer 29 feeds theinformation relating to the shift vectors via a context-sensitivedecoder 30 described in greater detail below to a motion compensator 31.Those parts of the video data stream containing information about theprediction errors are fed to a further context-sensitive decoder unit 30which reconstructs the prediction error data from the incoming bitstream. The prediction error data is fed to an inverse quantizer 32 andan inverse discrete cosine transformer 33 and added in an adder 34 tothe data delivered by the motion compensator 31 and stored in an imagememory 35. The image memory 35 is finally connected to a display unit 36in which the video images are displayed.

FIG. 5 shows the context-sensitive decoder unit 30 in detail. Thecontext-sensitive decoder unit 30 features a header parser 37 whichreads out the header information containing the bit stream and controlsa demultiplexer 38 so that the bit stream segments assigned to theindividual contexts are distributed on the decoding branches 39. The bitstream segments are in this case first written into the bit streambuffer 40 and subsequently converted by a Golumb decoder 41 into asequence of run length symbols which are transformed by a run lengthdecoder 42 into the assigned image symbols and written into the imagesymbol buffer 43. From the image symbol buffer 43 the image symbols areread out via a context switch 44 into a buffer 45 to which an imagecomposer 46 is connected downstream, in which the bit levels arereconstructed.

At the beginning of the transmission from the image symbol buffer 43into the buffer 45 the context switch 44 is in a preset position. As thereadout process proceeds the context switch 44 is controlled by acontext discriminator 47. The context discriminator 47 determines fromthe image symbols read out the context of the image symbol to be readout and switches the context switch 44 to the relevant decoding branch39. As with the context-sensitive encoding unit 4, a cluster unit 48 isalso present in the context-sensitive decoding unit 30 which evaluatesthe frequency distribution of the image symbols and by controlling thecontext discriminator 47 determines the assignment of the contexts tothe decoding branches 39. In addition the cluster unit 48 controls theGolumb decoder 41 in that the cluster unit 48 selects a Golumb codeadapted to the frequency distribution of the image symbols. The rulesunder which the cluster unit 48 operates must be the same as the rulesunder which the cluster unit 21 of the context-sensitive encoding unit 4operates so that the context switch 44 can operate the context-sensitivedecoder unit 30 in precisely the same way as context switch 18 operatesthe context-sensitive encoder unit 4.

Thus the context-sensitive decoder unit 30 executes the following steps:

-   -   Distribution of bit stream segments of the video data stream by        the demultiplexer 38 on decoding branches 39 which are each        assigned to an image symbol context;    -   Entropy decoding of the bit stream segments into run length        symbols;    -   Run length decoding of the run length symbols into video data of        image symbols representing an image;    -   Transmission of the image symbols on the decoding branches 39        via the context switch 44 into an image memory 45, in which case        the context switch 44 is in a prespecified position at a        prespecified time and is subsequently operated in accordance        with the context of the image symbols.

Finally, FIG. 6 shows a modified encoder 49 in which the transformedprediction error data is not tapped off immediately after the quantizer11 but after the context-sensitive decoder unit 4 and is transformedback by an inverse context-sensitive decoder unit 50, the inversequantizer 12 and the inverse discrete cosine transformer 13.

In conclusion it should be pointed out that the method described herecan be used whenever a statistical relationship exists between imagesymbols and a context. The image symbols can in this case be individualbits or also groups of bits in a bit level or extending over a pluralityof bit levels. This means that clustering of image symbols is alsopossible.

Finally it should be pointed out that the devices and methods describedhere for compression and decompression of video data streams can berealized both in hardware and in software. A mixed implementation isalso conceivable.

The method described here is in principle independent of the specificdata source and can thus be used to extend beyond the encoding of atexture to be used for encoding of administrative information.

The invention has been described in detail with particular reference topreferred embodiments thereof and examples, but it will be understoodthat variations and modifications can be effected within the spirit andscope of the invention.

The invention claimed is:
 1. A computer-implemented method forcompression of a video data stream, in which video data of an image isrepresented by image symbols, the method performed by logic instructionsembodied in computer hardware and comprising the steps of: reading imagesymbols out of an image memory; sorting the image symbols with the aidof a context switch on various encoding branches into image symbolgroups which are each assigned to different contexts, wherein the imagesymbols are sorted by: setting the context switch to a prespecifiedposition at a prespecified time; and activating the context, switchafter the pre-specified time, in accordance with the context of theimage symbol to be sorted; adaptively assigning particular contexts toparticular encoding branches by: evaluating the frequency distributionof image symbols sorted to different contexts; identifying two or morecontexts having a matching frequency distribution of image symbols; andgrouping the two or more identified contexts together, by assigning thetwo or more identified contexts together to a common encoding branch,such that sorting determinations for image symbols subsequently receivedat the context switch are influenced by frequency distributions ofpreviously sorted image symbols; and performing entropy encoding of theimage symbol groups and merging a data output by the respective encodingbranches into a compressed video data stream.
 2. A method according toclaim 1, wherein the image symbols existing in the individual encodingbranches are converted by a working length encoder assigned to therelevant encoding branch into working length symbols.
 3. A methodaccording to claim 2, wherein the working length symbols are convertedinto code symbols of the entropy code with the aid of an entropy codeadjusted to the frequency distribution of the image symbols in therelevant context.
 4. A method in accordance with claim 3, wherein thebit stream segments generated by entropy encoding are merged by amultiplexer to form a compressed video data stream.
 5. A method inaccordance with claim 1, wherein the entropy code used for convertingthe image symbols into code symbols is calculated analytically, and theentropy code is adapted to the frequency distribution of the imagesymbols in the relevant context.
 6. A method in accordance with claim 1,wherein a Golumb code is used for the entropy code.
 7. A method inaccordance with claim 1, wherein texture data is used for the video datato be compressed.
 8. A method in accordance with claim 1, whereininformation elements relating to the length of the bit stream segmentsis added into the compressed video data stream by the multiplexer.
 9. Amethod in accordance with claim 8, wherein the entropy code used forconverting the image symbols into code symbols is calculatedanalytically, and the entropy code is adapted to the frequencydistribution of the image symbols in the relevant context.
 10. A methodin accordance with claim 9, wherein a Golumb code is used for theentropy code.
 11. A method in accordance with claim 10, wherein texturedata is used for the video data to be compressed.
 12. A method accordingto claim 1, wherein the frequency distribution of the image symbolsrelates to a frequency at which the image symbols change in a predefinedportion of a video image.
 13. A device to compress a video data streamin which video data of an image is represented by image symbols,comprising: a context switch controlled by a context logic, the contextswitch being connected downstream of an image memory and being operableto read out image symbols from the image memory and further operable tosort the image symbols based on context into image symbol groups onvarious encoding branches, wherein the context switch is furtheroperable to be set to a prespecified position at a prespecified time,and operable to be activated after the prespecified time, in accordancewith the context of the image symbol to be sorted; a cluster unitoperable to evaluate the frequency distribution of image symbols sortedto different contexts, identify two or more contexts having a matchingfrequency distribution of sorted image symbols, and group the two ormore identified contexts together by assigning the two or moreidentified contexts together to a common encoding branch, such thatsorting determinations for image symbols subsequently received at thecontext switch are influenced by frequency distributions of previouslysorted image symbols; an entropy encoder to convert the image symbols ofthe image symbol groups; and a multiplexer to merge a code output of theentropy encoder to a compressed video data stream.
 14. Acomputer-implemented method for decompressing a compressed video datastream, in which the image symbols are extracted from the video datastream, the method performed by logic instructions embodied in computerhardware and comprising the steps of: dividing the video data streaminto bit stream segments which are each assigned to a context; entropydecoding the bit stream segments into image symbol groups; andtransmitting the image symbols in the image symbol groups distributedover various decoding branches via a context switch into an imagememory, wherein the context switch is in a prespecified position at aprespecified time, and the context switch is activated after thepre-specified time, in accordance with the context of the image symbols,wherein the decoding branches are adaptively assigned to the contexts ofthe image symbols during the decompression with the aid of a contextlogic which adaptively assigning particular contexts to particularencoding branches by evaluating frequency distribution of the imagesymbols in relevant contexts, identifying two or more contexts having amatching frequency distribution of image symbols, and grouping the twoor more identified contexts together by assigning the two or moreidentified contexts together to a common encoding branch, such thatsorting determinations for image symbols subsequently received at thecontext switch are influenced by frequency distributions of previouslysorted image symbols.
 15. A method in accordance with claim 14, whereinthe entropy decoder uses an analytically calculatable entropy code. 16.A method according to claim 15, wherein the run length symbols areencoded in Golumb code.
 17. A method in accordance with claim 14,wherein code symbols contained in the bit stream segments are convertedinto working length symbols by an entropy decoder, said working lengthsymbols then being decoded by a working length decoder into imagesymbols representing video data of an image.
 18. A method in accordancewith claim 14, wherein the bit stream segments of the video data streamare distributed by a demultiplexer to respective decoding branches whichare each assigned to a context.
 19. A method in accordance with claim14, wherein the demultiplexer is controlled by information elementsinserted into the data stream and relating to a length of the bit streamsegments.
 20. A method in accordance with claim 14, wherein texture datais used for the video data.
 21. A device to decompress a video datastream, comprising: a demultiplexer operable to sort the video datastream into bit stream segments onto various decoding branches assignedto different contexts of the image symbols; each decoding branchcomprising an entropy decoder operable to decode the bit stream segmentsinto image symbol groups, and a context switch arranged downstream ofsaid entropy decoders operable to transmit the image symbols in theimage symbol groups distributed over the various decoding branches intoan image memory; and a context logic controlling said context switch andbeing operable to evaluate the frequency distribution of the imagesymbols in respective contexts, and further operable to adaptivelyassign the decoding branches to the contexts of the image symbols duringdecompression based on the evaluated frequency distribution of the imagesymbols in the respective contexts, including: evaluating the frequencydistribution of image symbols, sorted to different contexts; identifyingtwo or more contexts having a matching frequency distribution of sortedimage symbols; and grouping the two or more identified contexts togetherby assigning the two or more identified contexts together to a commonencoding branch; such that sorting determinations for image symbolssubsequently received at the context switch are influenced by frequencydistributions of previously sorted image symbols.
 22. A device inaccordance with claim 21, wherein the decoding branches each have aworking length decoder.
 23. A device in accordance with claim 13,wherein the context switch is operable to distribute the image symbolsas a function of the context to different code branches, wherein eachcode branch has an entropy encoder for an entropy code adjusted to thefrequency distribution of the image symbols in relevant context and aworking length encoder and wherein each code branch is connected on anoutput side to a multiplexer.